{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making a Histogram\n",
    "To make a histogram in ggplot you have 2 options: `geom_histogram()` or `qplot`. `qplot` is a short-hand version of `geom_histogram`, so we'll start with `qplot` and then work towards a more complex example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `qplot`\n",
    "The easiest way to make a histogram is to pass data to the `qplot` function. By default if you give `qplot` only 1 variable, it will plot a histogram of that variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TjUzG5kbOZk7G5kbGZqejo6PVI8wbl09w3btw4cI1f82xsbGcOHHimvxB0orj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c14ce90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (273924449)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qplot(diamonds.price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`qplot` can also plot numpy arrays and lists. To do this, just pass in the array or list to the `qplot` function as you would with a pandas Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AUByRCwBAcUQuFGpycnLJt52amsqxY8cyNTW1ghMBwMVTb/YAwOpob2/Pzp07mz3Gijhw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\norX0RrI/vOsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1102006d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (274115921)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.random.normal(0, 1, 1000)\n",
    "qplot(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `geom_histogram`\n",
    "`geom_histogram` requires slightly more code, but it gives you more control over how your plots will look. In the example below, I'm going to make the same price histogram as I did above, but I'll use a ggplot base layer and add a `geom_histogram` to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wma+ZcsabZGxuZGx+5Gz2ZGx+ZGxullK+ZnUk4+Pj2bdvXzZu3Jh77703yXeK6aVLl9LT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1100c6e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (285317665)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(diamonds, aes(x='price')) + geom_histogram()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`geom_histogram` makes it easier to control other aesthetics of your plot. For example if you wanted the bars to be different colors based on the `cut` of the diamond, you could do that by adding a `fill='cut'` aesthetic to your base layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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plZaWatGiRdqwYYNqamqUlZWliooKSVJubq5mz56tdevWyW63q7y8PL6ko7y8POFSdQUF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fa1c610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (284826789)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(diamonds, aes(x='price', fill='cut')) + geom_histogram()"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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